Cell-type identification using multi-modal machine learning

The patent replaces the mechanical/chemical immunostaining process with a computational machine learning model that processes scRNA-seq data. The model uses gene expression profiles to predict cell types, substituting the physical antibody-based detection system with an information-processing system that achieves comparable or superior accuracy while enabling high-throughput analysis of thousands of cells simultaneously.

The patent creates a computational model that learns from training data containing paired scRNA-seq and immunostaining results. The model copies the cell-type classification capability from the gold standard immunostaining method into a predictive algorithm, allowing it to replicate accurate cell-type assignment without requiring actual protein staining for each cell.

The patent performs preliminary action by pre-training the machine learning model on a comprehensive reference dataset containing paired scRNA-seq and immunostaining data from multiple cell types. This pre-training phase captures general cell-type expression patterns and variability, enabling the model to achieve high accuracy on new datasets with minimal additional computational resources, thus reducing the complexity burden on end users.

The patent implements a dynamic two-stage approach where the model adapts its behavior based on input data characteristics. The system can operate in a fast prediction mode for routine analysis or switch to a more computationally intensive refinement mode when higher precision is needed, allowing flexible resource allocation that balances accuracy requirements against available computational resources.